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Record W4255374636 · doi:10.1504/ijitm.2017.086862

Enhancing BRICS integration: a cloud-based green supply chain concept

2017· article· en· W4255374636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Information Technology and Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsCarleton University
Fundersnot available
KeywordsCloud computingSupply chainIndustrialisationBusinessIndustrial organizationInternational tradeEconomicsMarketingPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

Today, BRICS - a five nation active group - represents an emerging power that aims to increase its economic integration in response to new global challenges. While many companies in the BRICS group are opening up new geographical industry clusters, changing their IT landscape, and contributing to global climate change concerns, others are not fully prepared, or ready, to do so. This study explores the applicability of mutually beneficial cloud-based green supply chain system among BRICS nations to help achieve development targets while mitigating the environmental impacts associated with rapid development and industrialisation. Data on the BRICS countries trade potential and patterns is reviewed to get a sense of the movement of goods and services between the BRICS nations. Although regulatory barriers and inter-country coordination pose significant challenges for meeting the promise of BRICS trade cooperation, the adoption of new cloud-based IT technologies, new innovations and new thinking remains an important enabling driver of green supply chain management and needs to be explored.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.242
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it